%In recent years, on one hand much attention has been paid to the representation
%of lexical meaning and the development of lexical-semantic resources on the other
The Web is evolving from a global information space of linked
documents to one where both documents and data are linked. Underpinning this
evolution is a set of best practices for publishing and connecting structured data
on the Web known as Linked Data~\cite{BizerHeathBerners-Lee09}. The Linked Open Data (LOD) project is bootstrapping the Web of Data by converting into RDF and publishing existing datasets available under open licenses.\newline
%Wealthy of data has implications. Firstly, a lot of data that are produced have problems
%in their structure; secondly large datasets are difficult to describe in ways that enable 
%their consumption: what is typically described by those data? how data are characteristically organized?\newline
%Vocabularies (aka ontologies) do not help much, since they provide a set of predicates
%and axioms, which is not tailored to the size and shape of data in the large; size and shape can only
%be empirically discovered.\newline
LOD is an ideal platform for empirical knowledge engineering research, since 
it has the critical amount of data for empirical research, data that are not necessarily
clean, optimized, or extensively structured. In practice, it's a perfect use case for
making \textit{patterns} emerge which can be studied by knowledge engineering and used for
the design, maintenance, and consumption of data.\newline
In addition, LOD datasets often contain a lot of natural language text, which is also important in order to make
advanced linking and exploration of data not only in the broad LOD cloud vision, 
but also in localized applications within large organizations that make use of linked data~\cite{DBLP:conf/ekaw/BaldassarreDGGST10}.\newline
Hybridizing natural language processing and semantic web techniques has therefore 
become an important research area. Part of the hybridization research, as well as part of the exploitation of LOD data, 
is carried out by means of lexical resources that are represented directly as linked data.
The major example is the WordNet RDF dataset~\cite{Schreiber:06:RRW}, which provides concepts
(called \textit{synsets}), each representing the sense of a set of synonymous words~\cite{gangemi:2003}.\newline
WordNet RDF has a low level of concept linking, because synsets are linked mostly by means of 
taxonomic (\textit{hyponymy}) relations, while LOD is mostly linked by means of domain relations, such as
parts of things, ways of participating in events or socially interacting, topics of documents, temporal and spatial references, etc.
Some lexical resources focus instead on domain relations as expressed in the lexicon of natural languages.\newline
This paper addresses hybridization research by porting the largest lexical resource for domain relations, FrameNet~\cite{Baker}, to the LOD cloud. \newline
FrameNet was previously available only as a lexical database, 
or as purely semantic web resources~\cite{Scheffczyketal, Coppola2009Frame}, derived from the lexical one: previous conversions to RDF are discussed in Sect. \ref{related-work}. After the release of version 1.5, the Berkeley FrameNet group asked us to produce a new version of FrameNet in RDF that can be optimized for use in the growing lexical part of the LOD cloud.\newline
%=======
%parts of things, ways of participating in events or socially interacting, topics of documents, temporal and spatial references, etc.\newline
Some lexical resources focus instead on domain relations as expressed in the lexicon of natural languages.
This paper addresses hybridization research by porting the largest lexical resource for domain relations, FrameNet~\cite{Baker}, to the LOD cloud. \newline
FrameNet is based on the notions of \textit{Semantic Frame}, \textit{Lexical Unit} (LU), and \textit{Frame Element} (FE): for example, the \textbf{Apply heat} frame refers to situations involving a \textbf{Cook} using a \textbf{Heating instrument} on some \textbf{Food} within some \textbf{Container}, etc. These types of involved entities are called FEs, and situations are expressed by words (\textit{lexemes}) that manifest a LU, e.g. \textbf{fry}, \textbf{cook}, \textbf{roast}, etc.
All those LUs are lexical counterparts of the semantic frame.\newline
 Intuitively, this is a more pragmatic and effective representation of lexical meaning, because frames focus on actual usage of language in real world situations, rather than on decontextualized terms as in traditional dictionaries (detailed analyses of the cognitive plausibility of frames as meaning units, besides \cite{Baker} itself, are \cite{gangemiOntolex, GangemiPresutti10}).\newline
FrameNet was previously available only as a lexical database, 
or as unlinked OWL resources~\cite{Scheffczyketal, Coppola2009Frame}, derived from former versions of the lexical database: such conversions are discussed in Sect. \ref{related-work}. After the release of version 1.5, the Berkeley FrameNet group asked us to produce a new version of FrameNet in RDF that can be optimized for use in the growing lexical part of the LOD cloud.\newline
%>>>>>>> .r22
Among lexical resources, FrameNet has been successfully employed in NLP applications that 
demonstrate its potential to improve the quality of question answering~\cite{ShenLapata07} or recognizing
textual entailment~\cite{BurchardtPennacchiotti}. \newline
Frames as a cognitive, linguistic, or knowledge representation primitive have been studied many times in the
last century (see \cite{gangemiOntolex} for an overview). For example~\cite{Minsky75} introduced frames into AI as a hub to factual and procedural knowledge: systems of interconnected frames would provide the
shifting perspectives or time-dependent change in a situation.
% ``frames would provide access to both factual and
%procedural knowledge, and systems of interconnected frames would provide the
%shifting perspectives or time-dependent change in a situation, as we move around
%or as some typical sequence of events unfolds''.
The intended meaning of a frame
across the different theories can be summarized as from~\cite{GangemiPresutti10}: \textit{a (small-sized and richly interconnected) structure, used to organize our knowledge, as well as to interpret, process or anticipate
information}.
In ontology design, frames are called \textit{knowledge patterns}~\cite{clark00knowledge, GangemiPresutti10},
as a special kind of design patterns. Following the approach outlined in~\cite{GangemiPresutti10}, we study frames 
as ``units of meaning'' for LOD and semantic web ontologies. \newline
%The Web of Data, with his canonical datasets
%(DBpedia, geographical and biological data, social network data,
%bibliographical, musical, and multimedia data, etc.), and the data emerging from
%the use of RDFa, Microformats, etc., has eventually provided an empirical basis
%to the semantic web, and indirectly to knowledge engineering. 
The contribution of this paper is twofold: (i) the production and publishing of a LOD
dataset for the FrameNet lexical database, and (ii) the description of a method to produce
knowledge patterns out of FrameNet frames.
For both contributions we use Semion~\cite{Semion}: a tool for ``triplifying"
non-RDF data into RDF models, and for refactoring RDF into other RDF or OWL customized 
models. The transformation process includes two
main steps: (i) a syntactic triplification of the original source and
(ii) a rule-based refactoring for adding semantics to triples. \newline
FrameNet as a LOD dataset provides new blood to the lexical grounding of semantic 
knowledge~\cite{GangemiPresutti10}, and boosts the ``lexical
linked data'' section of LOD, by linking FrameNet to other LOD datasets such as
WordNet RDF (section \ref{framenet-rdf}).\newline
%, in order to support the hybridization of NLP
%and semantic web methods and techniques.
As a further contribution, we introduce a rule-based method to select and refactor part of FrameNet as full-fledged OWL 
knowledge patterns to be used for ontology design and advanced exploration of LOD.
We discuss some non-trivial assumptions on how to interpret FrameNet relations as formal knowledge (section \ref{framenet-cp}).\newline
%
%Finally the extraction of content
%patterns from FrameNet allows, inter alia, to be able to reason on lexical
%knowledge assuming that it refers to domain knowledge, or to improve ontology
%design with reuse, shared good practices, or also to build resources of simple
%shared vocabularies for Linked Data.
The structure of the paper is the following. In section \ref{framenet} we summarize
the conceptual design of FrameNet. In section \ref{method}
we present the production of FrameNet as a LOD dataset. 
Section \ref{framenet-cp} describes experiences in refactoring frames as knowledge patterns. 
Final sections contain related work and conclusions.
%In section \ref{related-work} we discuss some related work. Finally, section \ref{discussion} is a
%discussion of results, lessons learnt, and future work.